ReMFPCA: Regularized Multivariate Functional Principal Component Analysis

Methods and tools for implementing regularized multivariate functional principal component analysis ('ReMFPCA') for multivariate functional data whose variables might be observed over different dimensional domains. 'ReMFPCA' is an object-oriented interface leveraging the extensibility and scalability of R6. It employs a parameter vector to control the smoothness of each functional variable. By incorporating smoothness constraints as penalty terms within a regularized optimization framework, 'ReMFPCA' generates smooth multivariate functional principal components, offering a concise and interpretable representation of the data. For detailed information on the methods and techniques used in 'ReMFPCA', please refer to Haghbin et al. (2023) <doi:10.48550/arXiv.2306.13980>.

Version:	1.0.0
Depends:	R (≥ 4.0), R6
Imports:	fda, expm, Matrix
Published:	2023-07-01
Author:	Hossein Haghbin [aut, cre], Yue Zhao [aut], Mehdi Maadooliat [aut]
Maintainer:	Hossein Haghbin <haghbin at pgu.ac.ir>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/haghbinh/ReMFPCA
NeedsCompilation:	no
Materials:	README
CRAN checks:	ReMFPCA results

Documentation:

Reference manual:

ReMFPCA.pdf

Downloads:

Package source:	ReMFPCA_1.0.0.tar.gz
Windows binaries:	r-devel: ReMFPCA_1.0.0.zip, r-release: ReMFPCA_1.0.0.zip, r-oldrel: ReMFPCA_1.0.0.zip
macOS binaries:	r-release (arm64): ReMFPCA_1.0.0.tgz, r-oldrel (arm64): ReMFPCA_1.0.0.tgz, r-release (x86_64): ReMFPCA_1.0.0.tgz, r-oldrel (x86_64): ReMFPCA_1.0.0.tgz

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